Improving Outpatient Psychiatric Appointment Attendance
Bibliographic record
Abstract
abstract: Mental health issues are a growing concern for individuals and the public. When patients do not attend their mental health appointments they place themselves at risk for poor health outcomes including worsening of symptoms, relapse, hospitalization, or danger to self and other behaviors. The breadth, background, and significance of this issue were investigated to determine a clinically relevant PICOT question. These elements of the PICOT question were investigated and high-quality evidence was gathered, analyzed, and synthesized in order to develop recommendations for an evidence-based project to help with no-shows at a non-profit integrated healthcare organization that is experiencing a high incidence of no-shows. The Quality Health Outcomes Model and Ottawa Model of Research Use guide the implementation and monitoring of the project. A chart review was completed in order to understand the impact of a novel automated reminder system on the no-show rate for all psychiatric appointments for 18 months. Additionally, demographic and appointment information was gathered to identify trends in the data and factors related to appointment status. The no-show rate significantly increased in 2019 with the new reminder system. No-shows occurred significantly more in males, tele-medicine appointments, and hospital discharge appointments. There were significant differences in no-show rates observed between reported races, with different providers, and at different practice locations. This gap analysis has provided insight into further projects and work to be completed in order to decrease no-shows, improve treatment compliance, produce better health outcomes, and increase revenue for this organization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".